Why Immutable Event Logs Beat Current State Snapshots

Event sourcing flips traditional database design by persisting every state change as an immutable event rather than overwriting current state. Instead of UPDATE queries that destroy history, you append facts like "OrderPlaced", "PaymentProcessed", or "ItemShipped" to an append-only log. This architectural choice makes temporal queries trivial. Want to know inventory levels last Tuesday? Just replay events up to that timestamp. Need a complete audit trail for compliance? Every state transition is already captured with full context and causality.

The append-only nature eliminates update anomalies and makes debugging production issues significantly easier. When a bug surfaces, you can replay the exact sequence of events that led to the corrupted state rather than guessing from a snapshot.

Snapshot Strategies for Fast State Reconstruction

Without optimization, rebuilding aggregate state means replaying thousands or millions of events from the beginning, creating unacceptable latency. Production systems solve this with periodic snapshots that checkpoint aggregate state at specific event sequence numbers.

Snapshot Strategy Comparison

When an aggregate reaches 100 events, store a snapshot with sequence number 100. Future rebuilds load snapshot 100 and replay only events 101 onward. This transforms full replay from seconds to milliseconds for high-activity aggregates.

Schema Evolution Without Breaking Historical Events

Events live forever in your system, but your domain model evolves. Adding a new field to "OrderPlaced" six months after launch means old events lack that field. Handling this requires explicit versioning strategies rather than hoping for backward compatibility.

Upcasting transforms old event versions into current schema during replay. When loading "OrderPlacedV1", an upcaster detects the version and maps it to "OrderPlacedV2" with default values for missing fields. The original event stays immutable in storage while runtime logic handles the transformation. Weak schema systems like JSON make this easier than strongly typed formats, but you lose compile-time safety.

Never modify historical events in place. The append-only guarantee is foundational to event sourcing. If you need to correct a mistake, append a compensating event like "OrderCancelled" rather than deleting the original "OrderPlaced".

Pairing CQRS for Read-Optimized Query Models

Event sourcing optimizes writes but makes queries expensive since you must rebuild state from events. CQRS (Command Query Responsibility Segregation) solves this by maintaining separate read models that project events into query-optimized schemas.

Write side stays event-sourced for auditability and temporal queries. Read side consumes the event stream and builds denormalized views in PostgreSQL, MongoDB, or Elasticsearch tuned for specific query patterns. An e-commerce system might maintain one read model for order history (normalized by customer) and another for inventory levels (normalized by warehouse). Each read model subscribes to relevant events and updates independently.

The trade-off is eventual consistency. Read models lag behind the write side by milliseconds to seconds depending on event processing throughput. For most business domains, this latency is acceptable compared to the flexibility of purpose-built query models.